Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells22209
Missing cells (%)12.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory144.0 B

Variable types

Numeric13
Categorical5

Warnings

countries has constant value "France" Constant
product_name has a high cardinality: 9453 distinct values High cardinality
brands has a high cardinality: 4223 distinct values High cardinality
ingredients_text has a high cardinality: 7791 distinct values High cardinality
df_index is highly correlated with Unnamed: 0High correlation
Unnamed: 0 is highly correlated with df_indexHigh correlation
energy_100g is highly correlated with calculated_energyHigh correlation
calculated_energy is highly correlated with energy_100gHigh correlation
nutrition_grade_fr is highly correlated with countriesHigh correlation
countries is highly correlated with nutrition_grade_frHigh correlation
ingredients_text has 2094 (20.9%) missing values Missing
nutrition_grade_fr has 396 (4.0%) missing values Missing
fat_100g has 1727 (17.3%) missing values Missing
saturated-fat_100g has 248 (2.5%) missing values Missing
carbohydrates_100g has 1784 (17.8%) missing values Missing
sugars_100g has 245 (2.5%) missing values Missing
fiber_100g has 3368 (33.7%) missing values Missing
salt_100g has 238 (2.4%) missing values Missing
nutrition-score-fr_100g has 396 (4.0%) missing values Missing
fruits-vegetables-nuts_100g has 9660 (96.6%) missing values Missing
calculated_energy has 1814 (18.1%) missing values Missing
product_name is uniformly distributed Uniform
ingredients_text is uniformly distributed Uniform
df_index has unique values Unique
Unnamed: 0 has unique values Unique
fat_100g has 729 (7.3%) zeros Zeros
saturated-fat_100g has 1304 (13.0%) zeros Zeros
carbohydrates_100g has 413 (4.1%) zeros Zeros
sugars_100g has 722 (7.2%) zeros Zeros
fiber_100g has 1943 (19.4%) zeros Zeros
proteins_100g has 662 (6.6%) zeros Zeros
salt_100g has 999 (10.0%) zeros Zeros
nutrition-score-fr_100g has 500 (5.0%) zeros Zeros

Reproduction

Analysis started2021-03-23 14:51:43.994731
Analysis finished2021-03-23 14:52:15.353422
Duration31.36 seconds
Software versionpandas-profiling v2.12.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45085.1871
Minimum1
Maximum91029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-03-23T15:52:15.521035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4387.45
Q122148
median44467.5
Q367997.75
95-th percentile86499.8
Maximum91029
Range91028
Interquartile range (IQR)45849.75

Descriptive statistics

Standard deviation26382.55195
Coefficient of variation (CV)0.5851711758
Kurtosis-1.20770192
Mean45085.1871
Median Absolute Deviation (MAD)22857
Skewness0.03169901282
Sum450851871
Variance696039047.3
MonotocityNot monotonic
2021-03-23T15:52:15.745035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
452591
 
< 0.1%
764801
 
< 0.1%
867111
 
< 0.1%
764721
 
< 0.1%
824551
 
< 0.1%
357651
 
< 0.1%
170851
 
< 0.1%
232301
 
< 0.1%
25281
 
< 0.1%
580491
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
11
< 0.1%
41
< 0.1%
101
< 0.1%
121
< 0.1%
201
< 0.1%
ValueCountFrequency (%)
910291
< 0.1%
910091
< 0.1%
909821
< 0.1%
909581
< 0.1%
909521
< 0.1%

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245783.8975
Minimum189
Maximum355977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-03-23T15:52:15.969721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum189
5-th percentile182962.75
Q1212010.5
median240782.5
Q3272517.75
95-th percentile338594.5
Maximum355977
Range355788
Interquartile range (IQR)60507.25

Descriptive statistics

Standard deviation50659.43284
Coefficient of variation (CV)0.2061137176
Kurtosis1.777024105
Mean245783.8975
Median Absolute Deviation (MAD)29976.5
Skewness-0.2291782459
Sum2457838975
Variance2566378135
MonotocityNot monotonic
2021-03-23T15:52:16.183491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2688081
 
< 0.1%
2731361
 
< 0.1%
2075841
 
< 0.1%
2554431
 
< 0.1%
2854141
 
< 0.1%
2649411
 
< 0.1%
2116901
 
< 0.1%
2424111
 
< 0.1%
2526541
 
< 0.1%
3366251
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
1891
< 0.1%
1941
< 0.1%
2421
< 0.1%
2491
< 0.1%
4621
< 0.1%
ValueCountFrequency (%)
3559771
< 0.1%
3558761
< 0.1%
3558111
< 0.1%
3557321
< 0.1%
3557191
< 0.1%

product_name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct9453
Distinct (%)94.8%
Missing33
Missing (%)0.3%
Memory size78.2 KiB
Huile d'olive vierge extra
 
10
Coquillettes
 
9
Couscous
 
7
Limonade
 
7
Mozzarella
 
6
Other values (9448)
9928 

Length

Max length126
Median length24
Mean length26.30972208
Min length3

Characters and Unicode

Total characters262229
Distinct characters138
Distinct categories17 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9097 ?
Unique (%)91.3%

Sample

1st rowEasy fruity
2nd rowLégumes Secs Gourmands
3rd rowRaifort doux d'Alsace
4th rowInstant choco
5th rowLe Pur Bœuf 5% de M.G.
ValueCountFrequency (%)
Huile d'olive vierge extra10
 
0.1%
Coquillettes9
 
0.1%
Couscous7
 
0.1%
Limonade7
 
0.1%
Mozzarella6
 
0.1%
Pois chiches6
 
0.1%
Corn Flakes6
 
0.1%
Jus d'ananas5
 
0.1%
Sirop de grenadine5
 
0.1%
Gnocchi à poêler5
 
0.1%
Other values (9443)9901
99.0%
(Missing)33
 
0.3%
2021-03-23T15:52:16.636981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de2499
 
5.9%
1026
 
2.4%
au843
 
2.0%
à635
 
1.5%
chocolat514
 
1.2%
et500
 
1.2%
aux466
 
1.1%
la452
 
1.1%
bio423
 
1.0%
lait394
 
0.9%
Other values (7147)34916
81.8%

Most occurring characters

ValueCountFrequency (%)
32988
 
12.6%
e26323
 
10.0%
a18834
 
7.2%
i15453
 
5.9%
r14471
 
5.5%
o14460
 
5.5%
s13923
 
5.3%
t13202
 
5.0%
u11063
 
4.2%
n11032
 
4.2%
Other values (128)90480
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter194023
74.0%
Space Separator32988
 
12.6%
Uppercase Letter26577
 
10.1%
Decimal Number3770
 
1.4%
Other Punctuation3006
 
1.1%
Dash Punctuation777
 
0.3%
Open Punctuation489
 
0.2%
Close Punctuation485
 
0.2%
Math Symbol76
 
< 0.1%
Other Symbol20
 
< 0.1%
Other values (7)18
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e26323
13.6%
a18834
9.7%
i15453
 
8.0%
r14471
 
7.5%
o14460
 
7.5%
s13923
 
7.2%
t13202
 
6.8%
u11063
 
5.7%
n11032
 
5.7%
l10797
 
5.6%
Other values (45)44465
22.9%
ValueCountFrequency (%)
C3408
12.8%
P2702
 
10.2%
S2203
 
8.3%
B2094
 
7.9%
M1932
 
7.3%
L1487
 
5.6%
F1446
 
5.4%
G1356
 
5.1%
T1263
 
4.8%
A1214
 
4.6%
Other values (25)7472
28.1%
ValueCountFrequency (%)
'954
31.7%
,810
26.9%
%440
14.6%
&365
 
12.1%
.197
 
6.6%
/120
 
4.0%
;43
 
1.4%
!24
 
0.8%
:22
 
0.7%
?16
 
0.5%
Other values (4)15
 
0.5%
ValueCountFrequency (%)
0965
25.6%
2619
16.4%
1553
14.7%
5405
10.7%
3323
 
8.6%
4315
 
8.4%
8195
 
5.2%
6185
 
4.9%
7149
 
4.0%
961
 
1.6%
ValueCountFrequency (%)
´5
71.4%
¨1
 
14.3%
`1
 
14.3%
ValueCountFrequency (%)
(482
98.6%
[5
 
1.0%
{2
 
0.4%
ValueCountFrequency (%)
+73
96.1%
|2
 
2.6%
=1
 
1.3%
ValueCountFrequency (%)
2
50.0%
Œ1
25.0%
œ1
25.0%
ValueCountFrequency (%)
)480
99.0%
]5
 
1.0%
ValueCountFrequency (%)
°12
60.0%
®8
40.0%
ValueCountFrequency (%)
¢1
50.0%
1
50.0%
ValueCountFrequency (%)
32988
100.0%
ValueCountFrequency (%)
-777
100.0%
ValueCountFrequency (%)
_1
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
º2
100.0%
ValueCountFrequency (%)
̀1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin220602
84.1%
Common41626
 
15.9%
Inherited1
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
e26323
 
11.9%
a18834
 
8.5%
i15453
 
7.0%
r14471
 
6.6%
o14460
 
6.6%
s13923
 
6.3%
t13202
 
6.0%
u11063
 
5.0%
n11032
 
5.0%
l10797
 
4.9%
Other values (81)71044
32.2%
ValueCountFrequency (%)
32988
79.2%
0965
 
2.3%
'954
 
2.3%
,810
 
1.9%
-777
 
1.9%
2619
 
1.5%
1553
 
1.3%
(482
 
1.2%
)480
 
1.2%
%440
 
1.1%
Other values (36)2558
 
6.1%
ValueCountFrequency (%)
̀1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII256023
97.6%
None6202
 
2.4%
Punctuation2
 
< 0.1%
Diacriticals1
 
< 0.1%
Currency Symbols1
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
32988
 
12.9%
e26323
 
10.3%
a18834
 
7.4%
i15453
 
6.0%
r14471
 
5.7%
o14460
 
5.6%
s13923
 
5.4%
t13202
 
5.2%
u11063
 
4.3%
n11032
 
4.3%
Other values (77)84274
32.9%
ValueCountFrequency (%)
é3782
61.0%
à635
 
10.2%
è538
 
8.7%
â300
 
4.8%
ê194
 
3.1%
û135
 
2.2%
ç90
 
1.5%
ô81
 
1.3%
œ75
 
1.2%
É72
 
1.2%
Other values (37)300
 
4.8%
ValueCountFrequency (%)
1
50.0%
1
50.0%
ValueCountFrequency (%)
̀1
100.0%
ValueCountFrequency (%)
1
100.0%

brands
Categorical

HIGH CARDINALITY

Distinct4223
Distinct (%)42.5%
Missing69
Missing (%)0.7%
Memory size78.2 KiB
Carrefour
 
317
Auchan
 
316
U
 
250
Casino
 
203
Leader Price
 
192
Other values (4218)
8653 

Length

Max length90
Median length9
Mean length11.29543853
Min length1

Characters and Unicode

Total characters112175
Distinct characters106
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3145 ?
Unique (%)31.7%

Sample

1st rowCarrefour Kids
2nd rowTipiak
3rd rowAlélor
4th rowNaturella
5th rowCharal
ValueCountFrequency (%)
Carrefour317
 
3.2%
Auchan316
 
3.2%
U250
 
2.5%
Casino203
 
2.0%
Leader Price192
 
1.9%
Picard127
 
1.3%
Cora108
 
1.1%
Monoprix81
 
0.8%
Franprix72
 
0.7%
Fleury Michon68
 
0.7%
Other values (4213)8197
82.0%
(Missing)69
 
0.7%
2021-03-23T15:52:17.083576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
carrefour438
 
2.7%
auchan359
 
2.2%
u310
 
1.9%
casino270
 
1.6%
la267
 
1.6%
de247
 
1.5%
leader230
 
1.4%
repère213
 
1.3%
price205
 
1.2%
bio203
 
1.2%
Other values (4405)13693
83.3%

Most occurring characters

ValueCountFrequency (%)
e11393
 
10.2%
a9608
 
8.6%
r9276
 
8.3%
i7904
 
7.0%
o6880
 
6.1%
6507
 
5.8%
n6194
 
5.5%
u4441
 
4.0%
s4377
 
3.9%
t4305
 
3.8%
Other values (96)41290
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter86120
76.8%
Uppercase Letter16845
 
15.0%
Space Separator6507
 
5.8%
Other Punctuation2412
 
2.2%
Decimal Number81
 
0.1%
Dash Punctuation67
 
0.1%
Open Punctuation58
 
0.1%
Close Punctuation58
 
0.1%
Math Symbol23
 
< 0.1%
Final Punctuation2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e11393
13.2%
a9608
11.2%
r9276
10.8%
i7904
9.2%
o6880
 
8.0%
n6194
 
7.2%
u4441
 
5.2%
s4377
 
5.1%
t4305
 
5.0%
l4238
 
4.9%
Other values (35)17504
20.3%
ValueCountFrequency (%)
C2009
11.9%
M1658
 
9.8%
L1573
 
9.3%
P1296
 
7.7%
B1229
 
7.3%
A1054
 
6.3%
S977
 
5.8%
D844
 
5.0%
F773
 
4.6%
R679
 
4.0%
Other values (23)4753
28.2%
ValueCountFrequency (%)
,1587
65.8%
'516
 
21.4%
&142
 
5.9%
.94
 
3.9%
!64
 
2.7%
?3
 
0.1%
;2
 
0.1%
/2
 
0.1%
%1
 
< 0.1%
:1
 
< 0.1%
ValueCountFrequency (%)
316
19.8%
113
16.0%
29
11.1%
79
11.1%
49
11.1%
08
9.9%
96
 
7.4%
55
 
6.2%
63
 
3.7%
83
 
3.7%
ValueCountFrequency (%)
1
50.0%
1
50.0%
ValueCountFrequency (%)
6507
100.0%
ValueCountFrequency (%)
-67
100.0%
ValueCountFrequency (%)
+23
100.0%
ValueCountFrequency (%)
(58
100.0%
ValueCountFrequency (%)
)58
100.0%
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin102965
91.8%
Common9208
 
8.2%
Han2
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
e11393
 
11.1%
a9608
 
9.3%
r9276
 
9.0%
i7904
 
7.7%
o6880
 
6.7%
n6194
 
6.0%
u4441
 
4.3%
s4377
 
4.3%
t4305
 
4.2%
l4238
 
4.1%
Other values (68)34349
33.4%
ValueCountFrequency (%)
6507
70.7%
,1587
 
17.2%
'516
 
5.6%
&142
 
1.5%
.94
 
1.0%
-67
 
0.7%
!64
 
0.7%
(58
 
0.6%
)58
 
0.6%
+23
 
0.2%
Other values (16)92
 
1.0%
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII110631
98.6%
None1540
 
1.4%
Punctuation2
 
< 0.1%
CJK2
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
e11393
 
10.3%
a9608
 
8.7%
r9276
 
8.4%
i7904
 
7.1%
o6880
 
6.2%
6507
 
5.9%
n6194
 
5.6%
u4441
 
4.0%
s4377
 
4.0%
t4305
 
3.9%
Other values (67)39746
35.9%
ValueCountFrequency (%)
é935
60.7%
è379
24.6%
ô47
 
3.1%
â43
 
2.8%
î21
 
1.4%
ê21
 
1.4%
É18
 
1.2%
ä13
 
0.8%
ü9
 
0.6%
ï8
 
0.5%
Other values (16)46
 
3.0%
ValueCountFrequency (%)
2
100.0%
ValueCountFrequency (%)
1
50.0%
1
50.0%

countries
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
France
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters60000
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance
2nd rowFrance
3rd rowFrance
4th rowFrance
5th rowFrance
ValueCountFrequency (%)
France10000
100.0%
2021-03-23T15:52:17.450782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T15:52:17.611624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
france10000
100.0%

Most occurring characters

ValueCountFrequency (%)
F10000
16.7%
r10000
16.7%
a10000
16.7%
n10000
16.7%
c10000
16.7%
e10000
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter50000
83.3%
Uppercase Letter10000
 
16.7%

Most frequent character per category

ValueCountFrequency (%)
r10000
20.0%
a10000
20.0%
n10000
20.0%
c10000
20.0%
e10000
20.0%
ValueCountFrequency (%)
F10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin60000
100.0%

Most frequent character per script

ValueCountFrequency (%)
F10000
16.7%
r10000
16.7%
a10000
16.7%
n10000
16.7%
c10000
16.7%
e10000
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII60000
100.0%

Most frequent character per block

ValueCountFrequency (%)
F10000
16.7%
r10000
16.7%
a10000
16.7%
n10000
16.7%
c10000
16.7%
e10000
16.7%

ingredients_text
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct7791
Distinct (%)98.5%
Missing2094
Missing (%)20.9%
Memory size78.2 KiB
Semoule de _blé_ dur de qualité supérieure.
 
16
Semoule de blé dur de qualité supérieure.
 
9
Haricots verts, eau, sel.
 
7
100 % semoule de _blé_ dur de qualité supérieure.
 
6
Lait demi-écrémé stérilisé UHT.
 
4
Other values (7786)
7864 

Length

Max length3856
Median length201
Mean length270.1463445
Min length3

Characters and Unicode

Total characters2135777
Distinct characters160
Distinct categories18 ?
Distinct scripts2 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7722 ?
Unique (%)97.7%

Sample

1st rowComposition / Samenstellin OF$is.sarteatx 0,15%1 sucre, acide cMqUe, antioxydant acide ()GeanJnatFeerde Frai &quot; m*des. gpnncentrattl), tropical smaak. Ingred'féflten. Ilit saponcentraten 12% (ananas 5306'sinaasappel 45% passievycht 0,6% ab(ikoos 0,306 guave 03, mandari!/or mi?eZUIF, stabi!lütbr. pétiœs
2nd rowSemoule de _blé_ dur précuite (_gluten_) 42%, farine de lentilles 23%, flocons de _soja_ 8%, pois cassés précuits déshydratés 7%, lentilles entières précuites déshydratées 6,5 %, pépites de _lupin_ 5,5%, flocons d'_orge_ (_gluten_), sel, huile de tournesol, carottes déshydratées, arômes.
3rd rowRacines de raifort, eau, huile de tournesol, amidon odifié, acidifiants : acétate de sodium, te vinaigre, sel, amidon m citique,épaississant comme xanthane, arômes naturels, conserra sorbate de disulfite de sodium, colorant : dioxyde Milder Meerrettich aus dem Elsass. Zutaten: -Meerrettichwurzeln,wax Sonnenblumenôl, Weizenstârke, Essig, Salz, modifizierte Sâuerungsmittel: Natriumacetat, Zitronensâure, Verdickungsm± Xanthan, natürliche Aromen, Konservierungsstoffe: Kalium* Natriummetabisulfit, Farbstoff: Titandicxid. Mild h?seradish from Alsace. IngredienÉ: hN3erafflsh roob, modifieds gum,naturai sorbate,sodium metabisu/flte, soentztitanium bis: siehe Nach dem ôffnen kühPlagerFifBèétt*re : see date on opened keep III I I I II I
4th row100% pure viande de bœuf.
5th row100 % semoule de _blé_ dur* de qualité supérieure. *issu de la filière Alpina Savoie.
ValueCountFrequency (%)
Semoule de _blé_ dur de qualité supérieure.16
 
0.2%
Semoule de blé dur de qualité supérieure.9
 
0.1%
Haricots verts, eau, sel.7
 
0.1%
100 % semoule de _blé_ dur de qualité supérieure.6
 
0.1%
Lait demi-écrémé stérilisé UHT.4
 
< 0.1%
Jus d'orange4
 
< 0.1%
Jus de pomme.4
 
< 0.1%
100 % semoule de blé dur de qualité supérieure.3
 
< 0.1%
Miel3
 
< 0.1%
Riz3
 
< 0.1%
Other values (7781)7847
78.5%
(Missing)2094
 
20.9%
2021-03-23T15:52:18.165420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de35392
 
10.9%
22107
 
6.8%
sel6315
 
1.9%
sucre4751
 
1.5%
lait4709
 
1.5%
eau4207
 
1.3%
et3802
 
1.2%
blé3717
 
1.1%
huile3214
 
1.0%
poudre3180
 
1.0%
Other values (24249)233140
71.8%

Most occurring characters

ValueCountFrequency (%)
318000
 
14.9%
e207074
 
9.7%
a125972
 
5.9%
r112677
 
5.3%
i111623
 
5.2%
s101589
 
4.8%
t97321
 
4.6%
o94378
 
4.4%
n87509
 
4.1%
,79142
 
3.7%
Other values (150)800492
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1485992
69.6%
Space Separator318081
 
14.9%
Other Punctuation137157
 
6.4%
Uppercase Letter78182
 
3.7%
Decimal Number55642
 
2.6%
Connector Punctuation20471
 
1.0%
Open Punctuation16322
 
0.8%
Close Punctuation15984
 
0.7%
Dash Punctuation7183
 
0.3%
Final Punctuation258
 
< 0.1%
Other values (8)505
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e207074
13.9%
a125972
 
8.5%
r112677
 
7.6%
i111623
 
7.5%
s101589
 
6.8%
t97321
 
6.5%
o94378
 
6.4%
n87509
 
5.9%
u74507
 
5.0%
l74503
 
5.0%
Other values (40)398839
26.8%
ValueCountFrequency (%)
E11580
14.8%
A5802
 
7.4%
S5747
 
7.4%
I5409
 
6.9%
C4504
 
5.8%
O4112
 
5.3%
T4022
 
5.1%
R3902
 
5.0%
P3818
 
4.9%
L3672
 
4.7%
Other values (33)25614
32.8%
ValueCountFrequency (%)
,79142
57.7%
%14790
 
10.8%
.12995
 
9.5%
:12868
 
9.4%
'6444
 
4.7%
*4304
 
3.1%
;2515
 
1.8%
/2109
 
1.5%
678
 
0.5%
?517
 
0.4%
Other values (7)795
 
0.6%
ValueCountFrequency (%)
19562
17.2%
09224
16.6%
27299
13.1%
56101
11.0%
35611
10.1%
45120
9.2%
64020
7.2%
73375
 
6.1%
82729
 
4.9%
92601
 
4.7%
ValueCountFrequency (%)
+165
64.5%
=49
 
19.1%
±30
 
11.7%
|7
 
2.7%
<4
 
1.6%
~1
 
0.4%
ValueCountFrequency (%)
(15631
95.8%
[608
 
3.7%
{71
 
0.4%
12
 
0.1%
ValueCountFrequency (%)
`5
55.6%
´2
 
22.2%
¨1
 
11.1%
^1
 
11.1%
ValueCountFrequency (%)
)15367
96.1%
]521
 
3.3%
}96
 
0.6%
ValueCountFrequency (%)
-7019
97.7%
148
 
2.1%
16
 
0.2%
ValueCountFrequency (%)
53
55.2%
«24
25.0%
19
 
19.8%
ValueCountFrequency (%)
228
88.4%
»28
 
10.9%
2
 
0.8%
ValueCountFrequency (%)
®12
50.0%
°11
45.8%
1
 
4.2%
ValueCountFrequency (%)
’1
33.3%
œ1
33.3%
1
33.3%
ValueCountFrequency (%)
318000
> 99.9%
 81
 
< 0.1%
ValueCountFrequency (%)
$85
76.6%
26
 
23.4%
ValueCountFrequency (%)
­1
50.0%
1
50.0%
ValueCountFrequency (%)
_20471
100.0%
ValueCountFrequency (%)
¹4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1564174
73.2%
Common571603
 
26.8%

Most frequent character per script

ValueCountFrequency (%)
e207074
13.2%
a125972
 
8.1%
r112677
 
7.2%
i111623
 
7.1%
s101589
 
6.5%
t97321
 
6.2%
o94378
 
6.0%
n87509
 
5.6%
u74507
 
4.8%
l74503
 
4.8%
Other values (83)477021
30.5%
ValueCountFrequency (%)
318000
55.6%
,79142
 
13.8%
_20471
 
3.6%
(15631
 
2.7%
)15367
 
2.7%
%14790
 
2.6%
.12995
 
2.3%
:12868
 
2.3%
19562
 
1.7%
09224
 
1.6%
Other values (57)63553
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2080199
97.4%
None54299
 
2.5%
Punctuation1157
 
0.1%
Alphabetic PF95
 
< 0.1%
Currency Symbols26
 
< 0.1%
Letterlike Symbols1
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
318000
15.3%
e207074
 
10.0%
a125972
 
6.1%
r112677
 
5.4%
i111623
 
5.4%
s101589
 
4.9%
t97321
 
4.7%
o94378
 
4.5%
n87509
 
4.2%
,79142
 
3.8%
Other values (83)744914
35.8%
ValueCountFrequency (%)
é37094
68.3%
ô4201
 
7.7%
è3061
 
5.6%
à2468
 
4.5%
â1496
 
2.8%
ï1202
 
2.2%
œ940
 
1.7%
É826
 
1.5%
ê396
 
0.7%
ü395
 
0.7%
Other values (44)2220
 
4.1%
ValueCountFrequency (%)
678
58.6%
228
 
19.7%
148
 
12.8%
53
 
4.6%
19
 
1.6%
16
 
1.4%
12
 
1.0%
2
 
0.2%
1
 
0.1%
ValueCountFrequency (%)
26
100.0%
ValueCountFrequency (%)
72
75.8%
23
 
24.2%
ValueCountFrequency (%)
1
100.0%

nutrition_grade_fr
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing396
Missing (%)4.0%
Memory size78.2 KiB
d
2659 
c
2076 
e
1939 
a
1525 
b
1405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9604
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowe
2nd rowa
3rd rowd
4th rowd
5th rowa
ValueCountFrequency (%)
d2659
26.6%
c2076
20.8%
e1939
19.4%
a1525
15.2%
b1405
14.1%
(Missing)396
 
4.0%
2021-03-23T15:52:18.551845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T15:52:18.695988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
d2659
27.7%
c2076
21.6%
e1939
20.2%
a1525
15.9%
b1405
14.6%

Most occurring characters

ValueCountFrequency (%)
d2659
27.7%
c2076
21.6%
e1939
20.2%
a1525
15.9%
b1405
14.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9604
100.0%

Most frequent character per category

ValueCountFrequency (%)
d2659
27.7%
c2076
21.6%
e1939
20.2%
a1525
15.9%
b1405
14.6%

Most occurring scripts

ValueCountFrequency (%)
Latin9604
100.0%

Most frequent character per script

ValueCountFrequency (%)
d2659
27.7%
c2076
21.6%
e1939
20.2%
a1525
15.9%
b1405
14.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII9604
100.0%

Most frequent character per block

ValueCountFrequency (%)
d2659
27.7%
c2076
21.6%
e1939
20.2%
a1525
15.9%
b1405
14.6%

energy_100g
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2245
Distinct (%)22.6%
Missing57
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1115.18276
Minimum0
Maximum3766
Zeros79
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-03-23T15:52:18.861747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile113
Q1431
median1040
Q31644
95-th percentile2377
Maximum3766
Range3766
Interquartile range (IQR)1213

Descriptive statistics

Standard deviation779.9659188
Coefficient of variation (CV)0.6994063637
Kurtosis-0.06547855236
Mean1115.18276
Median Absolute Deviation (MAD)608
Skewness0.6126035923
Sum11088262.18
Variance608346.8345
MonotocityNot monotonic
2021-03-23T15:52:19.087354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
079
 
0.8%
19247
 
0.5%
20133
 
0.3%
433
 
0.3%
370033
 
0.3%
18032
 
0.3%
17629
 
0.3%
18428
 
0.3%
376627
 
0.3%
20925
 
0.2%
Other values (2235)9577
95.8%
(Missing)57
 
0.6%
ValueCountFrequency (%)
079
0.8%
14
 
< 0.1%
1.43
 
< 0.1%
22
 
< 0.1%
36
 
0.1%
ValueCountFrequency (%)
376627
0.3%
37613
 
< 0.1%
37573
 
< 0.1%
37031
 
< 0.1%
370033
0.3%

fat_100g
Real number (ℝ≥0)

MISSING
ZEROS

Distinct674
Distinct (%)8.1%
Missing1727
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean13.46802738
Minimum0
Maximum100
Zeros729
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-03-23T15:52:19.305092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.1
median6.9
Q321
95-th percentile44.66
Maximum100
Range100
Interquartile range (IQR)19.9

Descriptive statistics

Standard deviation17.51559611
Coefficient of variation (CV)1.300531668
Kurtosis6.924325493
Mean13.46802738
Median Absolute Deviation (MAD)6.7
Skewness2.328063456
Sum111420.9905
Variance306.7961072
MonotocityNot monotonic
2021-03-23T15:52:19.519581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0729
 
7.3%
0.5332
 
3.3%
0.1245
 
2.5%
0.2184
 
1.8%
1108
 
1.1%
2105
 
1.1%
23104
 
1.0%
1197
 
1.0%
1591
 
0.9%
1282
 
0.8%
Other values (664)6196
62.0%
(Missing)1727
 
17.3%
ValueCountFrequency (%)
0729
7.3%
0.0012
 
< 0.1%
0.0112
 
0.1%
0.0151
 
< 0.1%
0.021
 
< 0.1%
ValueCountFrequency (%)
10059
0.6%
99.93
 
< 0.1%
99.84
 
< 0.1%
93.31
 
< 0.1%
92.71
 
< 0.1%

saturated-fat_100g
Real number (ℝ≥0)

MISSING
ZEROS

Distinct559
Distinct (%)5.7%
Missing248
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean5.230006527
Minimum0
Maximum100
Zeros1304
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-03-23T15:52:19.737159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.3
median1.9
Q37
95-th percentile20
Maximum100
Range100
Interquartile range (IQR)6.7

Descriptive statistics

Standard deviation8.305038471
Coefficient of variation (CV)1.587959485
Kurtosis20.92941021
Mean5.230006527
Median Absolute Deviation (MAD)1.89
Skewness3.590499788
Sum51003.02365
Variance68.973664
MonotocityNot monotonic
2021-03-23T15:52:19.967268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01304
 
13.0%
0.1565
 
5.7%
0.2291
 
2.9%
0.5269
 
2.7%
0.3243
 
2.4%
0.4229
 
2.3%
1217
 
2.2%
0.6177
 
1.8%
0.8159
 
1.6%
0.7143
 
1.4%
Other values (549)6155
61.6%
(Missing)248
 
2.5%
ValueCountFrequency (%)
01304
13.0%
0.00012
 
< 0.1%
0.0016
 
0.1%
0.0031
 
< 0.1%
0.0041
 
< 0.1%
ValueCountFrequency (%)
1001
< 0.1%
951
< 0.1%
911
< 0.1%
901
< 0.1%
871
< 0.1%

carbohydrates_100g
Real number (ℝ≥0)

MISSING
ZEROS

Distinct949
Distinct (%)11.6%
Missing1784
Missing (%)17.8%
Infinite0
Infinite (%)0.0%
Mean27.44811332
Minimum0
Maximum100
Zeros413
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-03-23T15:52:20.174574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median14.5
Q352.825
95-th percentile76.35
Maximum100
Range100
Interquartile range (IQR)48.825

Descriptive statistics

Standard deviation27.25703737
Coefficient of variation (CV)0.9930386493
Kurtosis-0.8944571594
Mean27.44811332
Median Absolute Deviation (MAD)13.6
Skewness0.7219970518
Sum225513.699
Variance742.9460864
MonotocityNot monotonic
2021-03-23T15:52:20.390040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0413
 
4.1%
0.5229
 
2.3%
1175
 
1.8%
11113
 
1.1%
12102
 
1.0%
273
 
0.7%
0.671
 
0.7%
0.870
 
0.7%
1568
 
0.7%
1367
 
0.7%
Other values (939)6835
68.3%
(Missing)1784
 
17.8%
ValueCountFrequency (%)
0413
4.1%
0.0011
 
< 0.1%
0.011
 
< 0.1%
0.0141
 
< 0.1%
0.021
 
< 0.1%
ValueCountFrequency (%)
1004
< 0.1%
99.91
 
< 0.1%
99.71
 
< 0.1%
99.62
< 0.1%
99.51
 
< 0.1%

sugars_100g
Real number (ℝ≥0)

MISSING
ZEROS

Distinct836
Distinct (%)8.6%
Missing245
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean13.33380468
Minimum0
Maximum100
Zeros722
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-03-23T15:52:20.594086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q317.9
95-th percentile56.7
Maximum100
Range100
Interquartile range (IQR)16.9

Descriptive statistics

Standard deviation19.12643104
Coefficient of variation (CV)1.434431619
Kurtosis3.331111484
Mean13.33380468
Median Absolute Deviation (MAD)3.73
Skewness1.922233602
Sum130071.2647
Variance365.8203644
MonotocityNot monotonic
2021-03-23T15:52:20.945628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0722
 
7.2%
0.5491
 
4.9%
1277
 
2.8%
2181
 
1.8%
0.7165
 
1.7%
0.6163
 
1.6%
3149
 
1.5%
0.1139
 
1.4%
0.8132
 
1.3%
0.9117
 
1.2%
Other values (826)7219
72.2%
(Missing)245
 
2.5%
ValueCountFrequency (%)
0722
7.2%
0.00011
 
< 0.1%
0.0016
 
0.1%
0.00191
 
< 0.1%
0.019
 
0.1%
ValueCountFrequency (%)
1004
< 0.1%
99.91
 
< 0.1%
99.61
 
< 0.1%
99.53
< 0.1%
99.31
 
< 0.1%

fiber_100g
Real number (ℝ≥0)

MISSING
ZEROS

Distinct317
Distinct (%)4.8%
Missing3368
Missing (%)33.7%
Infinite0
Infinite (%)0.0%
Mean2.719416435
Minimum0
Maximum99
Zeros1943
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-03-23T15:52:21.145419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.4
Q33.3
95-th percentile9.7
Maximum99
Range99
Interquartile range (IQR)3.3

Descriptive statistics

Standard deviation5.161008726
Coefficient of variation (CV)1.897836851
Kurtosis91.55494292
Mean2.719416435
Median Absolute Deviation (MAD)1.4
Skewness7.436731631
Sum18035.1698
Variance26.63601107
MonotocityNot monotonic
2021-03-23T15:52:21.352965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01943
19.4%
0.5263
 
2.6%
1186
 
1.9%
3170
 
1.7%
2151
 
1.5%
1.5134
 
1.3%
0.1122
 
1.2%
2.5106
 
1.1%
1.997
 
1.0%
0.994
 
0.9%
Other values (307)3366
33.7%
(Missing)3368
33.7%
ValueCountFrequency (%)
01943
19.4%
0.00011
 
< 0.1%
0.00071
 
< 0.1%
0.0017
 
0.1%
0.0021
 
< 0.1%
ValueCountFrequency (%)
991
< 0.1%
981
< 0.1%
891
< 0.1%
801
< 0.1%
76.251
< 0.1%

proteins_100g
Real number (ℝ≥0)

ZEROS

Distinct520
Distinct (%)5.2%
Missing80
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean7.781294667
Minimum0
Maximum100
Zeros662
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-03-23T15:52:21.561371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.59
median5.9
Q311
95-th percentile24
Maximum100
Range100
Interquartile range (IQR)9.41

Descriptive statistics

Standard deviation8.158415412
Coefficient of variation (CV)1.048465038
Kurtosis12.93757572
Mean7.781294667
Median Absolute Deviation (MAD)4.5
Skewness2.436764372
Sum77190.4431
Variance66.55974203
MonotocityNot monotonic
2021-03-23T15:52:21.773237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0662
 
6.6%
0.5391
 
3.9%
12160
 
1.6%
0.7160
 
1.6%
13155
 
1.6%
6143
 
1.4%
0.1137
 
1.4%
0.6136
 
1.4%
11129
 
1.3%
1126
 
1.3%
Other values (510)7721
77.2%
ValueCountFrequency (%)
0662
6.6%
0.00011
 
< 0.1%
0.0013
 
< 0.1%
0.0115
 
0.1%
0.021
 
< 0.1%
ValueCountFrequency (%)
1001
< 0.1%
881
< 0.1%
87.81
< 0.1%
85.21
< 0.1%
852
< 0.1%

salt_100g
Real number (ℝ≥0)

MISSING
ZEROS

Distinct987
Distinct (%)10.1%
Missing238
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean1.13986321
Minimum0
Maximum100
Zeros999
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-03-23T15:52:21.977841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.06
median0.53745
Q31.25
95-th percentile3.2
Maximum100
Range100
Interquartile range (IQR)1.19

Descriptive statistics

Standard deviation3.779008701
Coefficient of variation (CV)3.315317723
Kurtosis296.299645
Mean1.13986321
Median Absolute Deviation (MAD)0.51245
Skewness15.00190663
Sum11127.34465
Variance14.28090676
MonotocityNot monotonic
2021-03-23T15:52:22.185675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0999
 
10.0%
0.01427
 
4.3%
0.1396
 
4.0%
1273
 
2.7%
0.02197
 
2.0%
1.3181
 
1.8%
0.03171
 
1.7%
1.1171
 
1.7%
1.5170
 
1.7%
1.2160
 
1.6%
Other values (977)6617
66.2%
(Missing)238
 
2.4%
ValueCountFrequency (%)
0999
10.0%
0.00012
 
< 0.1%
0.000181
 
< 0.1%
0.000251
 
< 0.1%
0.00068581
 
< 0.1%
ValueCountFrequency (%)
1003
< 0.1%
97.51
 
< 0.1%
80.51
 
< 0.1%
801
 
< 0.1%
61.91
 
< 0.1%

nutrition-score-fr_100g
Real number (ℝ)

MISSING
ZEROS

Distinct48
Distinct (%)0.5%
Missing396
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean8.833402749
Minimum-13
Maximum35
Zeros500
Zeros (%)5.0%
Negative1529
Negative (%)15.3%
Memory size78.2 KiB
2021-03-23T15:52:22.378407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-13
5-th percentile-5
Q11
median9
Q315
95-th percentile24
Maximum35
Range48
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.906700757
Coefficient of variation (CV)1.008297823
Kurtosis-0.946583802
Mean8.833402749
Median Absolute Deviation (MAD)7
Skewness0.1644690042
Sum84836
Variance79.32931838
MonotocityNot monotonic
2021-03-23T15:52:22.580988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0500
 
5.0%
2488
 
4.9%
1469
 
4.7%
14467
 
4.7%
11436
 
4.4%
3433
 
4.3%
13414
 
4.1%
12390
 
3.9%
4373
 
3.7%
-1357
 
3.6%
Other values (38)5277
52.8%
(Missing)396
 
4.0%
ValueCountFrequency (%)
-131
 
< 0.1%
-122
 
< 0.1%
-115
0.1%
-109
0.1%
-912
0.1%
ValueCountFrequency (%)
351
 
< 0.1%
342
 
< 0.1%
322
 
< 0.1%
314
< 0.1%
306
0.1%

fruits-vegetables-nuts_100g
Real number (ℝ≥0)

MISSING

Distinct99
Distinct (%)29.1%
Missing9660
Missing (%)96.6%
Infinite0
Infinite (%)0.0%
Mean37.01079412
Minimum0
Maximum100
Zeros92
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-03-23T15:52:22.775380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median34.5
Q360
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)60

Descriptive statistics

Standard deviation34.44168914
Coefficient of variation (CV)0.9305849809
Kurtosis-1.021154761
Mean37.01079412
Median Absolute Deviation (MAD)30.2
Skewness0.4965712486
Sum12583.67
Variance1186.229951
MonotocityNot monotonic
2021-03-23T15:52:22.998428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
092
 
0.9%
5031
 
0.3%
10030
 
0.3%
1212
 
0.1%
6010
 
0.1%
6510
 
0.1%
556
 
0.1%
995
 
0.1%
105
 
0.1%
534
 
< 0.1%
Other values (89)135
 
1.4%
(Missing)9660
96.6%
ValueCountFrequency (%)
092
0.9%
1.51
 
< 0.1%
31
 
< 0.1%
3.51
 
< 0.1%
52
 
< 0.1%
ValueCountFrequency (%)
10030
0.3%
99.92
 
< 0.1%
99.71
 
< 0.1%
99.21
 
< 0.1%
995
 
0.1%

calculated_energy
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct6171
Distinct (%)75.4%
Missing1814
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean1103.202935
Minimum0
Maximum3968.2
Zeros83
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2021-03-23T15:52:23.358610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile107.1
Q1424.975
median1021.4
Q31631.975
95-th percentile2367
Maximum3968.2
Range3968.2
Interquartile range (IQR)1207

Descriptive statistics

Standard deviation780.5005442
Coefficient of variation (CV)0.7074859207
Kurtosis0.1249170228
Mean1103.202935
Median Absolute Deviation (MAD)602.95
Skewness0.6709130098
Sum9030819.226
Variance609181.0995
MonotocityNot monotonic
2021-03-23T15:52:23.565910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
083
 
0.8%
380052
 
0.5%
349618
 
0.2%
18716
 
0.2%
152113
 
0.1%
150412
 
0.1%
196.812
 
0.1%
20411
 
0.1%
17011
 
0.1%
212.511
 
0.1%
Other values (6161)7947
79.5%
(Missing)1814
 
18.1%
ValueCountFrequency (%)
083
0.8%
1.72
 
< 0.1%
3.47
 
0.1%
4.671
 
< 0.1%
5.12
 
< 0.1%
ValueCountFrequency (%)
3968.21
 
< 0.1%
3809.42
 
< 0.1%
3801.71
 
< 0.1%
3800.0341
 
< 0.1%
380052
0.5%

Interactions

2021-03-23T15:51:47.425858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:47.601512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:47.773124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:47.936871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:48.101478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:48.266030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:48.429172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:48.624611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:48.821328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:49.013073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:49.198584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:49.365910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:49.534576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:49.699796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:49.869745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:50.045181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:50.215160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:50.378892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:50.669800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:50.839206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:51.007005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:51.171232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:51.333910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:51.492218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:51.661725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:51.822127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:51.993577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:52.159577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:52.326046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:52.496233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:52.659924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:52.821939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:52.988301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:53.156499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:53.316294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:53.474328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:53.644663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:53.799973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:53.965910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:54.129707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:54.291426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:54.453771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:54.749679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:54.920382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:55.082885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:55.240661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:55.394079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:55.544623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:55.707480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:55.867145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:56.034394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:56.198723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:56.356409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:56.516858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:56.675913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:56.835577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:57.000868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:57.159797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:57.317076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:57.469975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:57.631926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:57.787344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:57.954386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:58.121797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:58.283758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:58.445728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:58.605643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:58.892306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:59.064618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:59.223136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:59.379292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:59.531564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:59.698853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:51:59.856743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:00.020792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:00.188240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:00.348092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:00.515388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:00.675699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:00.832938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:01.000188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:01.165596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:01.323972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:01.483105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:01.648816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:01.806053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:01.975653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:02.141940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:02.297764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:02.458618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:02.623254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:02.781620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:03.073980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:03.245080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:03.400642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:03.556063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:03.717000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:03.874338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:04.046929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:04.214390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:04.375916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:04.539499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:04.698857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:04.858793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:05.028211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:05.187528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:05.351490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:05.523515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:05.690228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:05.847790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:06.023460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:06.188771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:06.351486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:06.514823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:06.678739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:06.839227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:07.013720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:07.327091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:07.499320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:07.653639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:07.822392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:07.986820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:08.156419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:08.317610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:08.475953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:08.641479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:08.799503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:08.960167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:09.120570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:09.279151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:09.438002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:09.595445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:09.758317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:09.911309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:10.070405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:10.229608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:10.381349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:10.538556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:10.687950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:10.839243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:10.993181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:11.154205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:11.445332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:11.605154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:11.761594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:11.928243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:12.111161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:12.294232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:12.462555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:12.634056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:12.804736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:12.975228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:13.145284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:13.312080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:13.494843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-23T15:52:13.664920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-03-23T15:52:23.765440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-23T15:52:23.992664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-23T15:52:24.218185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-23T15:52:24.452003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-23T15:52:24.656866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-23T15:52:13.963791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-23T15:52:14.333659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-23T15:52:14.770205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-03-23T15:52:15.228401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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164476267489Légumes Secs GourmandsTipiakFranceSemoule de _blé_ dur précuite (_gluten_) 42%, farine de lentilles 23%, flocons de _soja_ 8%, pois cassés précuits déshydratés 7%, lentilles entières précuites déshydratées 6,5 %, pépites de _lupin_ 5,5%, flocons d'_orge_ (_gluten_), sel, huile de tournesol, carottes déshydratées, arômes.a1490.04.10.654.04.5NaN20.01.300-1.0NaN1413.8
239562233689Raifort doux d'AlsaceAlélorFranceRacines de raifort, eau, huile de tournesol, amidon odifié, acidifiants : acétate de sodium, te vinaigre, sel, amidon m citique,épaississant comme xanthane, arômes naturels, conserra sorbate de disulfite de sodium, colorant : dioxyde Milder Meerrettich aus dem Elsass. Zutaten: -Meerrettichwurzeln,wax Sonnenblumenôl, Weizenstârke, Essig, Salz, modifizierte Sâuerungsmittel: Natriumacetat, Zitronensâure, Verdickungsm± Xanthan, natürliche Aromen, Konservierungsstoffe: Kalium* Natriummetabisulfit, Farbstoff: Titandicxid. Mild h?seradish from Alsace. IngredienÉ: hN3erafflsh roob, modifieds gum,naturai sorbate,sodium metabisu/flte, soentztitanium bis: siehe Nach dem ôffnen kühPlagerFifBèétt*re : see date on opened keep III I I I II Id1172.024.32.618.02.8NaN1.21.50011.0NaN1249.8
366314270026Instant chocoNaturellaFranceNaNd1552.0NaN2.3NaN65.713.26.90.05011.0NaNNaN
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Last rows

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999086439338456Sauce tomate cuisinée ParmesanHeinzFranceTomates (157g de tomates pour 100g de sauce), huile de tournesol, sucre, amidon modifié, parmesan 1% (lait), sel, arôme, extraits d'ail et d'oignons.c323.03.60.69.27.4NaN1.50.80014.0NaN318.7
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999278187305352Tendre Gaufre au chocolat belgeLotusFranceFarine de _blé_, sucre, _œufs_, chocolat 16% (sucre, pâte de cacao, beurre de cacao, graisse butyrique (_lait_), émulsifiant (lécithine de _soja_, E476)), huiles végétales (colza, palme, noix de coco), sirop de glucose-fructose, fibre alimentaire (oligofructose), poudre de _lait_ écrémé, stabilisant (glycérol), sel, poudre à lever (diphosphate disodique, carbonate acide de sodium), arôme.e1830.021.27.454.433.23.26.10.960019.0NaN1834.1
999387530343471Jambon serrano chiffonadeJoada, Charcuterie CatalaneFranceINGREDiENTS: jambon de porc, sel, dextrose, conservateurs (E-252, E-250), antioxydant (E-300).d962.011.94.61.00.1NaN29.85.500016.0NaN975.8
999457686259180Marshmallowsgoût Vanille -Copains copinesFranceSirop de glucosefructose, sucre, eau, dextrose, gélatine de porc, arôme naturel de vanille, colorant : carmins.d1406.00.50.580.565.90.53.50.079014.00.01447.0
999540012234474Fromage frais 20%mg bio natureMaloFranceNaNa272.0NaN1.7NaN4.30.05.60.1000-2.0NaNNaN
999621624211368Soda saveur OrangeGrand JuryFranceEau gazéifiée ; jus de fruits à base de concentrés 12% (orange 10%, citron 2%) ; sucre ; acidifiant : acide citrique : arôme naturel d'orange et autres arômes naturels ; antioxydant : acide ascorbique ; colorant : caroténoïdes (E160a).e134.00.00.08.08.0NaN0.00.010011.012.0136.0
99979405194171Fromage blanc saveur vanilleActiviaFranceFromage blanc (lait), lait entier, eau, crème (lait), sucre (7,4%), sirop de glucose-fructose (0,7%), épaississants : E 1422 (amidon transformé), E 440 (pectine), E 412 (gomme guar), protéines de lait, arôme, gélatine (non porcine), correcteurs d'acidité: E330 (acide citrique), E333 (citrate de calcium), 331 (citrate de sodium), colorants : E 160a (caroténoides), E 101 (riboflavines), ferments lactiques dont bifidobacterium (Bifidus ActiRegularis) (lait), gousses de vanille épuisées. Décor : écorce de vanillec422.03.82.711.811.30.14.70.09003.0NaN424.9
999880624311941Confiture D'oranges AichaLes Conserves De MeknèsFranceNaNd1063.0NaN0.4NaN63.01.00.40.020012.0NaNNaN
999950095248832Thon Au Naturel AroAroFrancethon Listao (Katsuwonus pelamis), eau, sel.b414.00.60.50.00.0NaN23.51.20001.0NaN422.3